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Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques

Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in ord...

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Autores principales: Kim, Yong-Kyoung, Baek, Insuck, Lee, Kyung-Min, Kim, Geonwoo, Kim, Seyeon, Kim, Sung-Youn, Chan, Diane, Herrman, Timothy J., Kim, Namkuk, Kim, Moon S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10467122/
https://www.ncbi.nlm.nih.gov/pubmed/37505741
http://dx.doi.org/10.3390/toxins15070472
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author Kim, Yong-Kyoung
Baek, Insuck
Lee, Kyung-Min
Kim, Geonwoo
Kim, Seyeon
Kim, Sung-Youn
Chan, Diane
Herrman, Timothy J.
Kim, Namkuk
Kim, Moon S.
author_facet Kim, Yong-Kyoung
Baek, Insuck
Lee, Kyung-Min
Kim, Geonwoo
Kim, Seyeon
Kim, Sung-Youn
Chan, Diane
Herrman, Timothy J.
Kim, Namkuk
Kim, Moon S.
author_sort Kim, Yong-Kyoung
collection PubMed
description Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans.
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spelling pubmed-104671222023-08-31 Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques Kim, Yong-Kyoung Baek, Insuck Lee, Kyung-Min Kim, Geonwoo Kim, Seyeon Kim, Sung-Youn Chan, Diane Herrman, Timothy J. Kim, Namkuk Kim, Moon S. Toxins (Basel) Article Aflatoxins and fumonisins, commonly found in maize and maize-derived products, frequently co-occur and can cause dangerous illness in humans and animals if ingested in large amounts. Efforts are being made to develop suitable analytical methods for screening that can rapidly detect mycotoxins in order to prevent illness through early detection. A method for classifying contaminated maize by applying hyperspectral imaging techniques including reflectance in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, and fluorescence was investigated. Machine learning classification models in combination with different preprocessing methods were applied to screen ground maize samples for naturally occurring aflatoxin and fumonisin as single contaminants and as co-contaminants. Partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) with the radial basis function (RBF) kernel were employed as classification models using cut-off values of each mycotoxin. The classification performance of the SVM was better than that of PLS-DA, and the highest classification accuracies for fluorescence, VNIR, and SWIR were 89.1%, 71.7%, and 95.7%, respectively. SWIR imaging with the SVM model resulted in higher classification accuracies compared to the fluorescence and VNIR models, suggesting that as an alternative to conventional wet chemical methods, the hyperspectral SWIR imaging detection model may be the more effective and efficient analytical tool for mycotoxin analysis compared to fluorescence or VNIR imaging models. These methods represent a food safety screening tool capable of rapidly detecting mycotoxins in maize or other food ingredients consumed by animals or humans. MDPI 2023-07-22 /pmc/articles/PMC10467122/ /pubmed/37505741 http://dx.doi.org/10.3390/toxins15070472 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Yong-Kyoung
Baek, Insuck
Lee, Kyung-Min
Kim, Geonwoo
Kim, Seyeon
Kim, Sung-Youn
Chan, Diane
Herrman, Timothy J.
Kim, Namkuk
Kim, Moon S.
Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques
title Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques
title_full Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques
title_fullStr Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques
title_full_unstemmed Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques
title_short Rapid Detection of Single- and Co-Contaminant Aflatoxins and Fumonisins in Ground Maize Using Hyperspectral Imaging Techniques
title_sort rapid detection of single- and co-contaminant aflatoxins and fumonisins in ground maize using hyperspectral imaging techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10467122/
https://www.ncbi.nlm.nih.gov/pubmed/37505741
http://dx.doi.org/10.3390/toxins15070472
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